AILS-NTUA at SemEval-2024 Task 6: Efficient model tuning for hallucination detection and analysis
N Grigoriadou, M Lymperaiou, G Filandrianos… - arXiv preprint arXiv …, 2024 - arxiv.org
arXiv preprint arXiv:2404.01210, 2024•arxiv.org
In this paper, we present our team's submissions for SemEval-2024 Task-6-SHROOM, a
Shared-task on Hallucinations and Related Observable Overgeneration Mistakes. The
participants were asked to perform binary classification to identify cases of fluent
overgeneration hallucinations. Our experimentation included fine-tuning a pre-trained model
on hallucination detection and a Natural Language Inference (NLI) model. The most
successful strategy involved creating an ensemble of these models, resulting in accuracy …
Shared-task on Hallucinations and Related Observable Overgeneration Mistakes. The
participants were asked to perform binary classification to identify cases of fluent
overgeneration hallucinations. Our experimentation included fine-tuning a pre-trained model
on hallucination detection and a Natural Language Inference (NLI) model. The most
successful strategy involved creating an ensemble of these models, resulting in accuracy …
In this paper, we present our team's submissions for SemEval-2024 Task-6 - SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes. The participants were asked to perform binary classification to identify cases of fluent overgeneration hallucinations. Our experimentation included fine-tuning a pre-trained model on hallucination detection and a Natural Language Inference (NLI) model. The most successful strategy involved creating an ensemble of these models, resulting in accuracy rates of 77.8% and 79.9% on model-agnostic and model-aware datasets respectively, outperforming the organizers' baseline and achieving notable results when contrasted with the top-performing results in the competition, which reported accuracies of 84.7% and 81.3% correspondingly.
arxiv.org
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